Modeling peripheral olfactory coding in drosophila larvae

Modeling Peripheral Olfactory Coding in DrosophilaLarvaeDerek J. Hoare¤, James Humble, Ding Jin, Niall Gilding, Rasmus Petersen, Matthew Cobb, Catherine

McCrohan*

Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom

Abstract

The Drosophila larva possesses just 21 unique and identifiable pairs of olfactory sensory neurons (OSNs), enablinginvestigation of the contribution of individual OSN classes to the peripheral olfactory code. We combinedelectrophysiological and computational modeling to explore the nature of the peripheral olfactory code in situ. Werecorded firing responses of 19/21 OSNs to a panel of 19 odors. This was achieved by creating larvae expressing just onefunctioning class of odorant receptor, and hence OSN. Odor response profiles of each OSN class were highly specific andunique. However many OSN-odor pairs yielded variable responses, some of which were statistically indistinguishable frombackground activity. We used these electrophysiological data, incorporating both responses and spontaneous firing activity,to develop a Bayesian decoding model of olfactory processing. The model was able to accurately predict odor identity fromraw OSN responses; prediction accuracy ranged from 12%–77% (mean for all odors 45.2%) but was always significantlyabove chance (5.6%). However, there was no correlation between prediction accuracy for a given odor and the strength ofresponses of wild-type larvae to the same odor in a behavioral assay. We also used the model to predict the ability of thecode to discriminate between pairs of odors. Some of these predictions were supported in a behavioral discrimination(masking) assay but others were not. We conclude that our model of the peripheral code represents basic features of odordetection and discrimination, yielding insights into the information available to higher processing structures in the brain.

Citation: Hoare DJ, Humble J, Jin D, Gilding N, Petersen R, et al. (2011) Modeling Peripheral Olfactory Coding in Drosophila Larvae. PLoS ONE 6(8): e22996.doi:10.1371/journal.pone.0022996

Editor: Bradley Steven Launikonis, University of Queensland, Australia

Received March 16, 2011; Accepted July 6, 2011; Published August 9, 2011

Copyright: � 2011 Hoare et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: Funding was provided by a Biotechnology and Biological Sciences Research Council (http://www.bbsrc.ac.uk) studentship (DJH); a Medical ResearchCouncil (http://www.mrc.ac.uk) studentship (JH); The Royal Society (http://royalsociety.org) (CM and MC); Biotechnology and Biological Sciences Research Councilgrant BB/H009914/1 (CM and MC); and the CARMEN e-science project (Code analysis, repository, and modelling for e-Neuroscience; Engineering and PhysicalSciences Research Council (http://www.epsrc.ac.uk) grant EP/E002331/1; RP). The funders had no role in study design, data collection and analysis, decision topublish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

¤ Current address: NIHR National Biomedical Research Unit in Hearing, The University of Nottingham, Nottingham, United Kingdom

Introduction

In the peripheral olfactory system, odors are represented by a

combinatorial code comprising the responses of multiple classes of

olfactory sensory neurons (OSNs). Investigation of the contribu-

tion of individual OSNs to this code is hampered by complexity;

identification of specific OSNs in situ is difficult as most animals

possess tens or thousands of cells of each OSN class. In contrast,

the olfactory system of the Drosophila larva comprises 21 unique

pairs of OSNs, most expressing just a single class of olfactory

receptor (OR), and each projecting to its cognate glomerulus in the

larval antennal lobe [1–3]. We can record the electrophysiological

activity of individual OSNs in vivo, and the larva’s genetic

tractability enables analysis of the response profiles of individual,

identifiable OSNs expressing specific ORs [4]. This system

provides us with the possibility of describing the peripheral

olfactory code for a complete OSN population in an intact

organism.

In a previous study [4], we found that the firing responses of

identified larval OSNs to specific pure odors were variable. OSNs

of a given class responded reliably to some odors, but not to others.

This variability was consistent for specific odor-OSN pairs and was

not dependent on odor type or concentration, stimulus duration,

genotype or inter-individual differences [4]. Decisively, in larvae

expressing only two functional OSNs, one OSN class showed

100% responses to repeated, identical presentations of a given

odor, whilst the other OSN class showed variable (,100%)

responses to the same presentations of the same odor [4]. Thus, for

some odor-OSN pairs, firing responses vary to the extent that they

are sometimes statistically indistinguishable from background

‘noise’.

The response variability of individual OSNs implies that

information reaching the CNS from individual OSNs may be

ambiguous. We wished to explore how more reliable coding might

emerge at the population level. To address this, we chose a

Bayesian decoding approach [5] that would enable us to estimate

how much odor identity information can be extracted from OSN

activity by downstream neural circuits – in other words, how

accurately a target odor can be identified based on the raw

peripheral activity alone.

First, we exploited the ability to create larvae expressing just one

functioning class of OSN to characterise the electrophysiological

response profiles of 19 of the 21 OSNs to a panel of 19

biologically-relevant pure odors. This provided quantitative spike

PLoS ONE | www.plosone.org 1 August 2011 | Volume 6 | Issue 8 | e22996

frequency information, including both reliable and variable

responses. We then asked how the system could use this

information to identify target odors. We used the recorded firing

activity of identified OSNs during presentation of specific odors to

develop a computational model, incorporating both unambiguous

responses and ‘responses’ that were not statistically different from

spontaneous activity. Finally we explored how effective the model

was at discriminating between pairs of odors and tested its

predictions using behavioral assays.

Methods

Drosophila strainsThe w1118 strain was obtained from the Bloomington stock

centre. All other strains (OrX-Gal4, UAS-Orco and Orco2) were gifts

from Leslie Vosshall (Rockefeller University, NY). (Note that ‘Orco’

is the revised name for Or83b, and is applied to all orthologous

insect genes [6].) Larvae with one functional pair of OSNs (‘single-

functional-OSN’ lines) were created by making the appropriate

[OrX-Gal4/UAS-Orco; Orco2/2] crosses. This procedure is effec-

tive because the ORCO protein is a co-factor required for correct

expression of olfactory receptor proteins in Drosophila, so that

Orco2/2 larvae are anosmic [7]. Stocks were maintained at 25uCunder a 12:12 L:D cycle and fed on standard oatmeal and

molasses medium. We studied the individual responses of 20 OR

classes of single-functional-OSN larvae: Or1a, Or13a, Or22c,

Or24a, Or30a, Or33a, Or33b, Or35a, Or42a, Or42b, Or45a,

Or45b, Or47a, Or49a, Or59a, Or63a, Or67b, Or74a, Or82a and

Or83a. Or33b and Or 47a are expressed in the same OSN [7];

because the [OrX-Gal4/UAS-Orco; Orco2/2] line rescues Orco

function in whichever cell the OrX driver is expressed, both Or33b

and Or47a are rescued in the OSN when either the Or33b-Gal4 or

the Or47a-Gal4 driver is expressed. As a result, we were able to

study the responses of 19 of the 21 larval OSN classes. The 20

ORs (19 OSNs) tested here were the only ORs detected in larvae

of this strain using [OrX-Gal4/UAS-GFP] in a previous study [7].

In vivo electrophysiologyOur previously described method [4] was used. A third-instar

larva was picked from its rearing tube and immobilized on a

matchstick using parafilm. A glass microelectrode filled with

Drosophila larval ringer [8] was inserted into the cuticle at the base

of one of the paired dorsal organs (the sole sites of larval olfaction)

on the head. An earth was put into contact with the larval body

and a reference electrode inserted into the abdomen. Electrical

activity was acquired using a Neurolog system (Digitimer Ltd.,

UK); an AC preamp subtracted reference electrode activity from

the recording electrode activity, and the analog signal was

amplified and filtered before being converted to a digital signal

and analyzed offline using Spike2 software (Cambridge Electronic

Design, UK; see [4]). Each recording contained the activity of a

random sample of up to 8 of the 21 larval OSNs. We could

therefore record a specific OSN in around 30% of recordings [4].

OdorantsNineteen biologically-relevant pure odorants were used: five

alcohols (butanol, pentanol, hexanol, octanol and nonanol), six

aliphatic esters (ethyl acetate, propyl acetate, butyl acetate, iso-

amyl acetate, pentyl acetate and methyl caproate), two aromatics

(benzyl acetate and anisole) two alkyl aldehydes (heptanal and

octanal), one ketone (2-heptanone), one terpene (r-carvone) and

two organic acids (hexanoic and nonanoic acid). All were from

Sigma-Aldrich or BDH Laboratory Supplies and were of the

highest purity available. Odorants were mixed to a final

concentration of 2% with distilled water at room temperature

(,25uC) and shaken immediately before delivery to maintain an

even concentration. During recording air was delivered continu-

ously at a rate of 3 ml s21 from a 2 mm-diameter pipette tip

positioned 5 mm away from the head of the larva. The air was

bubbled through distilled water in a sealed conical flask prior to its

release. During stimulation, the air-stream was redirected for 1 s

through a second conical flask containing the odor solution, using

an electronically-controlled valve. Distilled water was used as a

control in all experiments. There was no effect of either odor

concentration or stimulus flow rate on the reliability of responses

for a given odor-OSN combination (see Figure S1).

Bayesian modelingTo determine how well the peripheral olfactory system can

identify and discriminate the odors used in our study, we used a

Bayesian decoding approach (reviewed in [5]). We wanted to

know how accurately a given target odor (chosen from a set of N

odors) could be identified (decoded) based on the spikes fired by

the OSN population.

We denote the N = 19 odors by oi (i = 1…19). Let P(oi) denote

the probability that the target odor on any given trial was odor oi. rjdenotes the number of action potentials fired by the jth OSN

(j = 1…15; all except Or45b and Or49a) in the time window of

duration 1 s, starting at stimulus onset. P(rj|oi) denotes the

conditional probability that the jth OSN fires rj spikes in response

to delivery of odor oi. r = [r1,r2,…,r15] denotes the population

response of all the OSNs on a given trial, and P(r|oi) the

conditional probability that the population response r occurs in

response to odor oi. Using Bayes’ identity, knowledge about which

odor was presented on a given trial can be gleaned from the

neuronal responses:

P(oijr)~P(rjoi)P(oi)

P(r)ð1Þ

Here P(oi|r) is the conditional probability that odor i was

delivered, given that population response r was observed.

P(r)~P

i

P(rjoi)P(oi) is the unconditional probability of the

population response. Since in this study the OSNs were recorded

individually, we approximated the conditional probability P(r|oi)

as a product of marginals P(rjoi)~PjP(rj joi).

We used a leave-one-out cross-validation approach. All trials

except one (for each OSN-odor combination) were first used to fit

the parameters of the model from the electrophysiological

recordings, as detailed below. Then the remaining trial was used

to test the performance of the model. To obtain reliable

performance data, this training-test cycle was repeated 4000

times. All data on decoding errors reported in Results were

obtained by this cross-validation procedure and are therefore

‘prediction errors’ (not ‘training errors’).We fitted the model

parameters in the following way. We found that the probabilities

P(rj|oi) could be accurately approximated by truncated Gaussian

distributions. By combining data across all larvae with a given

single functional OSN type, we had on average 8 trials (minimum

6) of the response of each OSN to delivery of each odor which

induced a significant response, according to the criteria detailed in

Results. We fitted the data for each of these OSN-odor

combinations individually using a truncated Gaussian; the

parameters of the truncated Gaussian were found by maximum

likelihood. To be conservative, we assumed that responses of an

OSN following stimulation with odors that were not ligands for

that OSN were random and fitted such combinations to a

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 2 August 2011 | Volume 6 | Issue 8 | e22996

truncated Gaussian, using spontaneous activity of the OSN (1 s

period prior to delivery of each odor, as well as responses to non-

ligand odors for that OSN).

The next step was to test the model’s performance. We

synthesized population responses r to a given target odor by, first,

identifying which OSNs exhibited a significant response to the

target odor and then, for each of these OSNs, randomly selecting

one of its responses to that odor. For the other OSNs (those with

no significant response to the target odor), one of the spontaneous

‘responses’ was selected. The maximum of the likelihoods P(oi|r)

was selected as the ‘predicted odor’. If the predicted odor matched

the target odor, this was counted as a correct decision, and vice

versa. To reliably estimate the probability of a correct decision, the

entire procedure was repeated 4000 times.

To determine chance levels of percent correct, we studied the

performance of the model under the null hypothesis that the

relationship between odors and responses was random. We

randomized the relationship between responses and which odors

had actually elicited them, refitted the parameters of the model,

and retested it as described above. By repeating this procedure

4000 times, we determined an upper (p = 0.01) confidence level

(6.96%) above which predictions could be considered to be

statistically significant.

BehaviorWe used a standard locomotor assay to quantify the response of

larvae to individual odors. Briefly, at least 20 third instar larvae

were placed at the centre of a 9 cm diameter Petri dish that was

covered with 2.5% agar. 2.5 ml of an undiluted odor source was

loaded onto a small circle of filter paper placed on the lid of an

Eppendorf tube situated on one side of the dish. The dish was

placed on a grid that divided the plate into two halves, with a 10-

mm diameter central start zone. The lid of the dish was replaced

and after 5 minutes the distribution of the larvae in the three zones

(attracted, repulsed, not responding) was noted. A behavioral

response index ((natt2nrep/ntot)6100) was then calculated [10]. To

estimate how well larvae discriminate between two odors, we

carried out a masking experiment [9], in which the behavioral

locomotory response to a point source of odor A was tested in the

absence and presence of a background of odor B. The latter was

generated by evenly spacing 561 ml aliquots of odor B on a 9 cm

filter paper, which rested between the Petri-dish and the lid. Odor

A was placed on an Eppendorf lid on one side of the dish as

described above.

Results

Spontaneous and odor-evoked firing activity in identifiedOSNs

We used 20 single-functional-OSN lines to investigate spontane-

ous (background) activity and responses to odors in identified OSNs.

Reliably identifying the activity of a rescued OSN in these lines was

straightforward when there was a response to an odor, as only one

OSN showed altered activity. The OSN could then be simply

tracked through the same recording on the basis of its unique action

potential amplitude and shape [4]. We never detected more than

one responding OSN within a given recording and therefore

concluded that in every case this was the rescued OSN.

In the absence of odor stimulation OSN classes showed different

and varying levels of spontaneous activity (Table 1). The mean

spontaneous activity of rescued OSNs varied from 0.760.2 Hz

(Or33b/47a) to 7.961.6 Hz (Or83a) with an overall range of 0–

27 Hz; these values encompass the range of spontaneous activity

for OSNs from non-genetically manipulated w1118 larvae (x =

7.660.62 Hz; n = 296, range = 0–31; [4]), and of unresponsive

OSNs in Orco2/2 larvae (x = 3.6360.5 Hz; n = 15, range = 1–15).

Nineteen pure odors were each tested on at least 60 OSNs from

each of the 20 single-functional-OSN lines. Once a ligand had

been identified for a particular OSN class, we were able to know if

the rescued OSN was present in any particular recording. For

three OSN classes – Or22c, Or33a and Or82a – we were unable

to identify any ligands using electrophysiology. Odors were

delivered as 1s pulses, in a random order, with a 2 min inter-

stimulus interval. Specific odor-OSN combinations that gave a

response were presented at least eight times. Typical electrophys-

iological recordings for three OSN classes and four odors are

shown in Figure 1A; the traces for each OSN class were from a

single larva and also included spontaneous activity in other, non-

functional OSNs. There was no evidence that the activity of a

responding OSN altered the spontaneous activity of the non-

functional Orco2/2 OSNs, confirming our previous report [4].

There were no significant correlations between the maximum

absolute activity observed for each OSN class during odor

stimulation and the maximum level of spontaneous activity

(r15 = 2.052, p = 0.847) (Table 1), between maximum absolute

activity during odor stimulation and mean spontaneous activity

(r15 = 0.024, p = 0.929), or between maximum activity during a

response and mean spontaneous activity (r15 = 0.006, p = 0.983),

indicating that response intensity in a given OSN was independent

of its level of spontaneous activity.

For some odor-OSN combinations, responses were highly

variable (Figure 1B,C). In the light of this, together with variations

in spontaneous activity, it was important to provide a criterion to

define a significant response. We used our previously-described,

probabilistic ‘response criterion’ for which the 0.05 probability of

making a Type 1 error in describing the activity of a particular

OSN as a ‘response’ corresponds to a change in firing rate of

65 Hz during stimulation as compared to the extremes of

spontaneous activity seen in that OSN in each of the 10 s prior

to stimulation [4]. This objective, probabilistic definition, based on

the known activity of a particular OSN, is preferable to an

arbitrary threshold, or to no criterion at all. For each odor-OSN

pair we calculated the percentage of responses to a given odor that

exceeded the criterion, i.e. those for which the change in firing rate

was statistically distinguishable from noise. The data are

summarized in Figure 2 (and presented in full in supplementary

Figures S2 and S3, which also include the mean response intensity

– spike count – above criterion).

60/361 (16.6%) of the odor-OSN combinations studied

produced a response. As expected, the same response profile was

generated by the rescue of Or33b and of Or47a, which are co-

expressed in the same OSN [7]. Both these lines were activated

only by pentyl acetate, and did not share this profile with any other

single-functional OSN line. No two OSN profiles were the same,

confirming that each of the larval OSN classes is unique, and that

our data represent the responses of 19 of the 21 larval OSNs.

Octanal did not activate any OSN, while pentyl acetate caused

excitation in seven OSN classes. Some OSN classes responded to

no odor (n = 3), or up to 13 odors (n = 1), but most showed

considerable selectivity in their response profile; 11/19 OSN

classes responded to just 1–3 odors.

Using our stimulus regime, the majority of odor-OSN response

profiles were variable (i.e. responses were above criterion on

,100% of trials, Figure 2), and most (57/60) were excitatory.

Only three odor-OSN combinations produced inhibition; the

Or49a OSN was inhibited by butanol and 2-heptanone

(Figure 1A), while the Or67b OSN was inhibited by anisole.

The remaining 301 odor-OSN combinations consistently yielded

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 3 August 2011 | Volume 6 | Issue 8 | e22996

no response. There was no significant correlation between mean

response intensity (spikes/s) and the frequency with which a

response above criterion was observed for each odor/OSN

combination (r56 = .245, p = 0.066).

A Bayesian decoding model of the peripheral codeOur consistent finding of considerable variation in responses of

OSNs to specific odors raised the question of whether the

robustness of odor discrimination is increased by integration of

information at the population level. We hypothesized that reliable

information transmission emerges at the ensemble level by

integrating the responses of multiple OSNs. To test this

hypothesis, we constructed a Bayesian decoding model that

integrates the responses of multiple OSNs in a statistically efficient

manner. We used our electrophysiological data from single-

functional-OSN larvae to develop the model (see Methods). We

included the actual spike count for each functional OSN during

1 s stimulation with each odor, regardless of the level of

spontaneous activity. Thus the model incorporated responses as

well as spontaneous (background) activity of both responding and

non-responding OSNs, which all together contribute to the

combinatorial code for a given odor. Four OSNs were excluded

due to shortage of (Or45b), or lack of (Or22c, Or33a and Or82a)

electrophysiological data. One odor (octanal) was excluded

because no single-functional-OSN strain showed an electrophys-

iological response to it. The input to the model on any given trial

therefore consisted of randomly selected samples of the activity of

each of 15 OSNs during presentation of one of 18 target odors.

The corresponding output of the model was a prediction of the

odor most likely to have elicited the input – the model was

effectively required to identify the input as one of the target odors.

The fact that the OSN responses exhibit considerable variability in

their response to a given odor, makes this a demanding 18-

alternative forced choice task; chance level was 5.6%. The model’s

performance for each of the 18 target odors is plotted in Figure 3A.

Despite the considerable variability of the firing responses for most

odor-OSN combinations, every target was significantly correctly

predicted. The mean accuracy of stimulus identification across all

odors was 45.260.2%, eight times greater than chance. However,

not all odors were equally well predicted. The most accurate

prediction level was for 2-heptanone (76.860.3% of trials); the

least accurate was for pentanol (12.360.2%). The model was

particularly efficient at detecting aliphatic esters (ethyl…pentyl

acetate) (range = 49.160.4% to 69.460.3%).

To determine the robustness of the peripheral odor represen-

tation, we examined the effect of progressively eliminating

individual OSNs. At each stage we recomputed the prediction

accuracy based on all 15 subsets of 14 cells and determined the

OSN whose removal produced least performance decrement. This

OSN was then eliminated. This procedure was repeated until only

a single OSN remained (Figure 3B). The first OSN to be removed

was Or33b/47a, which had virtually no effect on the accuracy of

the model (following removal of this OSN, the model’s accuracy

actually increased from 42.2360.08% to 45.2260.09%), suggest-

ing that information from this OSN is not necessary for the

detection of the odors studied here (this OSN responded only to

pentyl acetate, which was detected by five other OSNs). Indeed,

the first five OSNs (Or33b/47a, Or83a, Or74a, Or35a and

Table 1. Summary of electrophysiological activity of single olfactory sensory neurons (OSNs) in Drosophila larvae.

Spontaneous activity (Hz) Firing rate during stimulation (Hz)

Response above criterion Absolute

OSN Mean SE n Min. Max. Mean SE n Min. Max. Min. Max.

Or1a 3.2 0.8 24 0 16 4 0.8 30 0 21 0 37

Or13a 1.5 0.2 24 0 4 7.9 2.1 18 0 30 0 59

Or24a 7.3 0.8 24 0 22 9.6 1.1 46 29 38 0 51

Or30a 3.8 0.3 24 0 6 11.1 1.4 10 0 67 0 74

Or33b/47a 0.7 0.2 24 0 3 10 0.5 8 3 41 2 50

Or35a 7 0.9 24 0 20 16.4 1.4 16 232 109 0 115

Or42a 4 0.8 24 0 17 41.6 1 38 0 133 0 137

Or42b 5.5 1.1 24 0 18 14.8 1.4 12 0 80 0 104

Or45a 2.7 0.8 24 0 8 6.1 0.5 22 0 19 0 28

Or45b 1.9 0.4 24 0 6 23.5 1.4 8 4 45 9 53

Or49a 5.2 0.9 24 0 18 23.2 0.4 21 215 0 0 10

Or59a 1.2 0.3 24 0 4 17.6 1 17 0 61 0 69

Or63a 1.2 0.3 24 0 5 15.9 1 14 0 43 0 49

Or67b 6 0.4 24 0 15 8.1 0.4 36 0 44 1 48

Or74a 4.9 0.5 24 0 12 15.1 0.9 22 28 51 0 60

Or83a 7.9 1.6 24 0 27 14.8 0.8 14 0 33 3 47

Spontaneous activity = the activity of a single OSN in the second prior to each olfactory stimulation. Firing rate = the activity of a single OSN during 1 second stimulationwith one of 19 odors. Min. and max. denote the minimum and maximum rates observed. Response above criterion = change in spike frequency above/below theprobabilistic response criterion (a change of 65 Hz during stimulation as compared to the spontaneous activity seen in that OSN in the 10 s prior to stimulation – seeMaterials and Methods). Absolute = absolute OSN activity during stimulation. Larvae with a single functional OSN were [OrX-Gal4/UAS-Orco ; Orco2/2], constructedfollowing the protocol in [7]. Or33b and Or47a are co-expressed in the same neuron, so their data were pooled. To be certain that spontaneous activity was obtainedfrom a functional OSN, a response to at least one odor had to be detected in that OSN. No responses were detected for Or22c, Or33a and Or82a, so there are no data forthese three classes of OSN.doi:10.1371/journal.pone.0022996.t001

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 4 August 2011 | Volume 6 | Issue 8 | e22996

Or42a) were removed from the model with only a slight decline in

its accuracy (from 42.2360.08% to 40.8460.08%). There was no

significant correlation between the order in which OSNs were

removed from the model and number of odors to which they

responded (r14 = 0.362, p = 0.204), showing that the efficiency of

the model is not just based on the number of odors that each OSN

can detect. Not surprisingly, as OSNs were removed from the

model, certain odors could no longer be predicted at all. For

example, when only Or63a was left, the overall (mean) accuracy of

the model was 14.7360.03%, but this OSN was unable to

correctly identify some odors, such as butanol….nonanol.

Testing the model using behavioral assaysAlthough the model was based on data collected from 19 OSNs,

rather than the full complement of 21, and despite obvious

differences in stimulus duration (1 s for electrophysiology vs 5 min

for behavior), we decided to explore whether the model could

nevertheless make useful predictions about behavioral responses to

odors. We tested responses of wild-type, 21-functional OSN w1118

larvae to individual odors using a mass locomotory assay. The

results are shown in Figure 4. A significant response index – either

attraction or repulsion - was obtained for 15 out of 19 odors. To

explore any relationship between the model’s accuracy in predicting

a target odor (Figure 3A) and the strength of the behavioral response

(response index, either positive or negative) to that odor, we carried

out a correlation analysis (octanal was omitted, since this was not

used to generate the model). There was no correlation between the

two parameters. Some odors that were predicted well by the model

yielded a weak behavioral response (e.g. propyl acetate, 2-

heptanone, ), and vice versa (e.g. pentanol, hexanol).

To further explore the ability of the model to reflect behavior,

we examined how well a Bayesian model could discriminate

Figure 1. Electrophysiological activity of single, identified OSNs during 1 s stimulation (bar) with odors. Recordings are from single-functional-OSN larvae. A. Each trace for a given OR class is from the same larva. The Or35a OSN was activated by butanol and propyl acetate, butshowed no response to anisole or 2-heptanone; the Or49a OSN was inhibited by butanol and 2-heptanone, but showed no response to the other twoodors; the Or59a OSN was activated by anisole, but showed no response to the other three odors. B, C. The Or35a OSN showed variable responses toboth butyl acetate and hexanol. B and C were recorded from separate larvae, each of which showed a response (top trace) or no response (lowertrace) to identical presentations of an odor.doi:10.1371/journal.pone.0022996.g001

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 5 August 2011 | Volume 6 | Issue 8 | e22996

between pairs of odors: that is, when the model’s target was odor

A, how often the OSN activity profile of that odor could be

distinguished from that of odor B. For each odor pair of interest,

we constructed a Bayesian model based on the 15 OSNs identified

above and trained it to discriminate between the two odors. (The

procedures were otherwise identical to those used above). Figure 5

presents a discrimination matrix showing the ability of the model

to discriminate all possible odor pairs. The model was presented

with OSN responses to each pair of odors and required to

discriminate between them. Chance performance was 50%. Every

odor pair was discriminated above this level and, with 11

exceptions, showed a discrimination value of $75%. The lowest

levels of discrimination were generally found between structurally

similar pairs of odors (e.g. pentanol/hexanol – 63–66%). Within

functional groups, the highest levels of discriminability were

detected between hexanoic and nonanoic acid (98%), while the

most consistent discriminability was seen within the four

homologous aliphatic esters (ethyl… pentyl acetate; 86–96%).

We tested the output of the discrimination matrix by

studying the behavior of wild-type larvae. We used a ‘masking

test’ [9], in which wild-type larvae were required to detect a test

odor in the presence of a continuous background of the other,

masking, odor. We chose pairs of odors that were either poorly

discriminated by the model (benzyl acetate and hexanol;

pentanol and hexanol; ethyl acetate and heptanal – 63–68%

discriminability; Figure 6A–C) or that were well discriminated

(butyl acetate and octanol; hexanoic acid and hexanol;

hexanoic acid and pentanol – 98–100% discriminability;

Figure 7A–C). If two odors are hard to distinguish, larvae

should find it difficult to detect the test odor against the

background masking odor; the task should be easier for odor

pairs that are easy to distinguish.

In some cases the model was apparently a good predictor of

behavioral discrimination. For example, two of the three odor

pairs that were predicted by the model to be poorly discriminated

- benzyl acetate and hexanol, and pentanol and hexanol - were

also poorly discriminated in the masking test (Figure 6A,B).

However, for the heptanal/ethyl acetate pairing, responses to

each odor were not significantly reduced in the presence of the

other as a mask, demonstrating good behavioral discrimination

(Figure 6C). Behavioral response indices to butyl acetate and

octanol, and to hexanol and hexanoic acid were all significantly

reduced in the presence of the other odor as a mask (Figure 7A,B),

despite the model’s prediction of good discrimination between

these odor pairs. The third pair of odors, predicted to be well

discriminated, yielded asymmetrical data in the masking test. The

response to hexanoic acid was not significantly reduced in the

presence of a pentanol mask, whereas in the reciprocal test the

response to pentanol was significantly reduced in the presence of

hexanoic acid (Figure 7C). These data show that predictions

arising from our model of peripheral ONS coding are not always

correlated with behavioral odor discrimination, and that

discrimination as measured by the masking test is not reciprocal

for every odor pair.

Figure 2. Summary of electrophysiological responses of identified OSNs to a panel of 19 odors. Blue squares indicate inhibition; red tobrown show excitation. Numbers in ‘response reliability’ key indicate the percentage of times that an identified OSN responded to a given odor,rounded up to the nearest 10%. OR33b and OR47a are co-expressed in the same OSN (indicated in blue) and show identical response profiles.doi:10.1371/journal.pone.0022996.g002

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 6 August 2011 | Volume 6 | Issue 8 | e22996

Figure 3. Decoding results of the Bayesian model of peripheral processing. A. The model was presented with responses to each of the 18odors (‘target’) and had to identify which odor induced the response profile. The graph shows the mean percentage of trials (6 SEM) on which themodel correctly identified the target, using 4000 simulations for each odor. Dashed line indicates the percentage expected by chance, grey band

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 7 August 2011 | Volume 6 | Issue 8 | e22996

Discussion

In this study we present the first description of peripheral

olfactory coding in a near-complete (19/21) population of OSNs,

and in an intact organism. We extend our previous finding that

many odor-OSN combinations yield highly variable responses;

based on an objective probabilistic criterion, many ‘responses’ are

not statistically different from spontaneous changes in background

firing activity in the same neuron [4]. Response uncertainty in the

peripheral olfactory system has been reported for other organisms.

Mouse MOR71 cells responded consistently to acetophenone but

showed qualitative response variability to benzaldehyde [11];

similar differences have been reported in MOR23 cells [12]. In

Anopheles mosquitoes, TE1A OSNs respond .80% of the time to

4-ethylphenol, but ,20% of the time to pentanoic acid [13]. In

the vast majority of organisms, where there are many neurons

within each OSN class, quantitative and qualitative variability in

responses provides a continuum of overall response intensity

within the class. However, the Drosophila larva has only a single

pair of OSNs in each class, so must‘cope’ with variability as an

integral part of the peripheral code.

In apparent contrast to our findings, Asahina et al. [14]

recorded odor-evoked calcium signals in OSN axon terminals

within the larval antennal lobe and found that responses were

predictable (invariant) for a given odor-OSN combination.

However, axonal calcium imaging does not provide a complete

reflection of firing activity in sensory dendrites. For example, the

adult Drosophila antennal lobe shows presynaptic peptidergic

suppression of calcium signals [15]. It is possible that similar

presynaptic modulation also occurs in the larval brain, providing

an initial step towards ‘sharpening up’ an apparently unreliable

primary code.

Our study was an in vivo and in situ investigation of larval OSN

function. Kreher et al. [9,16] used a Drosophila ectopic expression

system (the ‘empty neuron’ – larval ORs are expressed in adult

OSNs in which normal adult OR expression is prevented) to study

the ligand specificity of larval ORs. These two experimental

approaches used different odor delivery methods, response

criteria, life-stages, background strains of Drosophila, cellular

contexts and odor/receptor combinations. Nevertheless, for the

14 ORs and eight odors shared by our studies there was a large

degree of agreement. More than 70% of the findings of the two

studies were the same in terms of the odors that did/did not induce

a response in a particular OSN class. Of the remaining odor-OR

combinations that produced a response in one study but not the

other, most (68%) were cases where Kreher et al. [9] reported a

response and we did not. These authors reported more instances of

inhibitory responses than we found; this is partly due to their

response criterion (using a different criterion, they described fewer

examples of inhibition in their earlier report [16]). Differences in

neuronal context might also account for differences in the nature

of responses recorded.

Guo and Kim [17] modelled the function of Drosophila OR

molecules by comparing electrophysiological data from the empty

neuron preparation [18] and the protein sequence data of each

class of OR. This investigation suggested that Drosophila ORs

contain a pocket into which odor molecules bind and provided

some insight into specific binding sites, in particular for

unbranched primary alcohols (methanol… octanol) and the

inhibitory response of the adult receptor Or47b. Combining our

unique in situ data set for larval ORs and our model of OSN

activity with this approach [17] might shed further light on OR

and OSN function, in particular the differences observed between

ORs expressed at either and both life-stages of this insect.

Our Bayesian modeling approach explored how accurate odor

discrimination might emerge from an ensemble code incorporat-

ing variable responses. Both these responses and the unmodulated,

spontaneous activity of non-responding OSNs must together form

the overall code. It was therefore important for our model to

incorporate all firing activity that occurred during presentation of

a given odor, whether in responding or non-responding OSNs.

This ensured that activity of the whole OSN population was taken

into account when predicting target odors. Despite the variability

in both levels of spontaneous activity and responses of OSNs, the

model was able (on average eight times better than chance) to

identify all of the odors tested from the raw electrophysiological

data. These results are conservative, since they include informa-

tion only about the number of spikes fired in a one second time

window. Normally the brain would have access to a much richer

sample of peripheral activity than this, including the temporal

pattern of spikes, and over longer stimulation periods. Taking this

into account, the model was surprisingly good. It also confirmed

that the peripheral code is distributed; loss of activity from one or a

few OSNs only slightly reduced its ability to accurately predict

odors. The model did not identify all odors with equal accuracy.

The most reliably identified were the ecologically significant

homologous aliphatic esters (ethyl… pentyl acetate), encouraging

corresponds to P,0.01 confidence limits (see Methods). B. Relative contribution of each OSN class to the accuracy of the model. Data show meanaccuracy levels with a progressive reduction in the number of OSN classes in the model. Standard errors are smaller than the size of the data points.OSN classes contributing least to the ability of the model to accurately predict odors were iteratively removed. The number of odors detected by theremoved OSN class is given underneath each OSN label. Dashed line indicates chance level of correct odor prediction. The X-axis shows the numberof OSNs in the model, with the full model (n = 15) as the first value.doi:10.1371/journal.pone.0022996.g003

Figure 4. Behavioral responses of wild-type w1118 larvae to 19odors. Mean behavioral response indices 6 SEM. Larvae werestimulated with a point source of odor in a mass behavioral test.Response indices were compared with a theoretical value of zero usingone-sample t-tests. * = P#.05; ** = P#.01; ** = P#.001. n = 8 assays perodor.doi:10.1371/journal.pone.0022996.g004

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 8 August 2011 | Volume 6 | Issue 8 | e22996

us to think that the model does indeed reflect important aspects of

sensory processing in this organism.

In creating the model, our aim was to generate a representation

of the peripheral code. However, it was still interesting to explore

its potential to predict behavior. There was no correlation between

the model’s ability to identify a target odor and the behavioral

response index for the same odor. Similarly, when we tested

discrimination between pairs of odors, the model was not a reliable

predictor for behavioral discrimination. These findings were not

surprising, and may have a number of explanations. First, the

model may not adequately reflect the peripheral code owing to the

limitations in our data set referred to earlier. In particular, we were

able to record from only 19 of the 21 larval OSNs. The two

‘missing’ OSNs were present in the 21-functional OSN larvae used

in the masking test, and may be decisive for accurate identification

and discrimination of some or all of these odors. Second, the odor

presentation regimes (timing, concentration) differed between

electrophysiological and behavioral tests and this would be

expected to influence the measured output. Third, behavioral

output reflects the integrative processing of olfactory information

by the brain, whereas the model was based on peripheral activity

alone. The ability to detect and correctly identify a given odor is

necessary, but not sufficient, to elicit a behavioral response to that

odor. The latter also depends on the adaptive and behavioral

relevance of the odor and may present as either attraction,

repulsion, or no behavioral response. Brain processing could also

explain how an odor pair that is poorly discriminated by the

peripheral model (for example, heptanal/ethyl acetate) is much

better discriminated by the whole animal; in this case, discrim-

ination must be sharpened up centrally. In the adult fly brain, odor

detection is sharpened by differential amplification and modula-

tion of signals from OSNs and their cognate glomerulus, together

with fast and rapidly accommodating firing responses in projection

neurons [19]. Lateral inhibition between glomeruli, which

sharpens the signal by increasing the signal:noise ratio, appears

to be particularly important [20]; intraglomerular inhibition may

also play a role [21]. There are similar structures in the larval

antennal lobe [22], and the output of OSNs and glomeruli is

modulated by inhibitory local interneurons and projection

neurons, at least one of which mediates concentration-invariant

odor perception [14]. Such central processing could be used not

only to enhance detection of individual odors but also to improve

discrimination between odors. In the mammalian olfactory bulb,

enhanced cholinergic neurotransmission both sharpens the

olfactory receptive fields of mitral cells and increases behavioral

pairwise odor discrimination [23]. A further observation from the

behavioral masking tests was that reciprocal odor discrimination

could be asymmetrical. In this test, the two odors are presented

differently – one as a background odor and the other a localised

source. The effects of these two kinds of presentation on odor

gradients within the plate could influence the way in which each is

perceived by the brain.

We conclude that, even if the model were deemed to be a fairly

good representation of the basic peripheral code, assuming that its

Figure 5. Ability of the Bayesian model to discriminate between pairs of odors. The model was presented with OSN responses to each pairof odors and required to discriminate between them. The table shows the % of times an odor pair was correctly discriminated; each discriminationwas run through the model 4000 times. Values are not strictly reciprocal on either side of the diagonal because of the sampling method used by themodel. Chance discrimination = 50%.doi:10.1371/journal.pone.0022996.g005

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 9 August 2011 | Volume 6 | Issue 8 | e22996

output is directly translating into odor-induced behavior implies

that important and essential aspects of central processing will be

overlooked. However, the model was able to identify a range of

ecologically relevant odors on the basis of the peripheral responses

they induce, supporting the view that the peripheral code can

perform this task, albeit crudely, without the need for central

Figure 6. Testing the Bayesian model using behavioral discrimination of odor pairs. Masking experiment for pairs of odors predicted bythe model to be poorly discriminated (63–68% discriminability). Control w1118 larvae were tested in a mass olfactory experiment, presented with alocalised odor (‘test’) and a masking odor (‘mask’). For full details, see text. * = P,0.01, *** = P,0.001, n.s. = not significant. n = 8 assays per condition.doi:10.1371/journal.pone.0022996.g006

Figure 7. Testing the Bayesian model using behavioral discrimination of odor pairs. Masking experiment for pairs of odors predicted bythe model to be well discriminated (98–100% discriminability). Control w1118 larvae were tested in a mass olfactory experiment, presented with alocalised odor (‘test’) and a masking odor (‘mask’). For full details, see text. * = P,0.01, *** = P,0.001, n.s. = not significant. n = 8 assays per condition.doi:10.1371/journal.pone.0022996.g007

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 10 August 2011 | Volume 6 | Issue 8 | e22996

processing. An interesting aim for future research will be to

explore further how far the initial olfactory code embodied by our

Bayesian model places constraints on olfactory behaviour, thus

providing insight into the nature and function of central

processing.

Supporting Information

Figure S1 Variable responses for a given odor-OSNcombination are not a function of stimulus flow rate orconcentration. Individual w1118 larvae (1–4) were stimulated

with 2% butanol at three different flow rates (A) or 0.2% butanol

at 30 ml/s (B). In all cases, butanol induced qualitative response

variability; sometimes the OSNs responded, sometimes they did

not. For clarity, responses are ordered in terms of whether there

was a response or not; there was no order effect; stimuli were

presented in random order.

(TIF)

Figure S2 Electrophysiological responses of identifiedlarval OSNs. Larvae from nine single Or strains (Or1a – Or45a)

were stimulated with 19 odors. Responses are given as mean (6

SEM) firing rates of single OSNs above an objective response

criterion. Percentages indicate the proportion of odor presenta-

tions that elicited a response above criterion when stimulated with

a given odor, in preparations in which the functional OSN had

been identified by showing a response to another odor (there are

no percentages for the Or33b OSN, which responded to only a

single odor). n$8 tests per odor/OSN combination. (Data for

Or13a, Or42a and Or42b are taken from [4], Figure 5).

(TIF)

Figure S3 Electrophysiological responses of identifiedlarval OSNs. Larvae from eight single Or strains (Or45b –

Or83a) were stimulated with 19 odors. Responses are given as

mean (6 SEM) firing rates of single OSNs above an objective

response criterion. Percentages indicate the proportion of odor

presentations that elicited a response above criterion when

stimulated with a given odor, in preparations in which the

functional OSN had been identified by showing a response to

another odor (there are no percentages for the Or45b and Or47a

OSNs, which responded to only a single odor). n$8 tests per

odor/OSN combination.

(TIF)

Author Contributions

Conceived and designed the experiments: DJH JH RP MC CM.

Performed the experiments: DJH JH DJ NG. Analyzed the data: DJ JH

RP MC CM. Contributed reagents/materials/analysis tools: RP MC CM.

Wrote the paper: DJ RP MC CM.

References

1. Ramaekers A, Magnenat E, Marin EC, Gendre N, Jefferis GSXE, et al. (2005)Glomerular maps without cellular redundancy at successive levels of the

Drosophila larval olfactory circuit. Curr Biol 15: 982–992.2. Masuda-Nakagawa LM, Gendre N, O’Kane CJ, Stocker RF (2009) Localized

olfactory representation in mushroom bodies of Drosophila larvae. Proc Natl AcadSci USA 102: 10314–10319.

3. Gomez-Marin A, Duistermars BJ, Frye MA, Louis M (2010) Mechanisms of

odor-tracking: multiple sensors for enhanced perception and behaviour. FrontCell Neurosci 4: 6.

4. Hoare DJ, McCrohan CR, Cobb M (2008) Precise and fuzzy coding by olfactorysensory neurons. J Neurosci 28: 9710–9722.

5. Quian Quiroga R, Panzeri S (2009) Extracting information from neuronal

populations: information theory and decoding approaches. Nat Revs Neurosci10: 173–185.

6. Vosshall LB, Hansson BS (2011) A unified nomenclature system for the insectolfactory co-receptor. Chem Senses 36: 497–498.

7. Fishilevich E, Domingos AI, Asahina K, Naef F, Vosshall LB, et al. (2005)

Chemotaxis behavior mediated by single larval olfactory neurons in Drosophila.Curr Biol 15: 2086–2096.

8. Jan LY, Jan YN (1976) Properties of the larval neuromuscular junction inDrosophila melanogaster. J Physiol 262: 189–214.

9. Kreher SA, Mathew D, Kim J, Carlson JR (2008) Translation of sensory inputinto behavioral output via an olfactory system. Neuron 59: 110–24.

10. Cobb M (1999) What and how do maggots smell? Biol Rev 74: 425–459.

11. Bozza T, Feinstein P, Zheng C, Mombaerts P (2002) Odorant receptorexpression defines functional units in the mouse olfactory system. J Neurosci 22:

3033–3043.12. Grosmaitre X, Vassalli A, Mombaerts P, Shepherd GM, Ma M (2006) Odorant

responses of olfactory sensory neurons expressing the odorant receptor MOR23:

A patch clamp analysis in gene-targeted mice. Proc Natl Acad Sci USA 103:1970–1975.

13. Qiu YT, van Loon JJA, Takken W, Meijerink J, Smid HM (2006) Olfactorycoding in antennal neurons of the malaria mosquito Anopheles gambiae. Chem

Senses 31: 845–863.14. Asahina K, Louis M, Piccinotti S, Vosshall LB (2009) A circuit supporting

concentration-invariant odor perception in Drosophila. J Biol 8: 9.

15. Ignell R, Root CM, Birse RT, Wang JW, Nassel DR, et al. (2009) Presynapticpeptidergic modulation of olfactory receptor neurons in Drosophila. Proc Natl

Acad Sci USA 106: 13070–13075.16. Kreher SA, Kwon AY, Carlson JR (2005) The molecular basis of odor coding in

the Drosophila larva. Neuron 46: 445–456.

17. Guo S, Kim J (2010) Dissecting the molecular mechanism of drosophila odorantreceptors through activity modeling and comparative analysis. Proteins 78:

381–399.18. Hallem EA, Carlson JR (2006) Coding of odors by a receptor repertoire. Cell

125: 143–160.

19. Bhandawat V, Olsen SR, Gouwens NW, Schlief ML, Wilson RI (2007) Sensoryprocessing in the Drosophila antennal lobe increases reliability and separability of

ensemble odor representations. Nat Neurosci 10: 1474–1482.20. Olsen SR, Wilson RI (2008) Lateral presynaptic inhibition mediates gain control

in an olfactory circuit. Nature 452: 956–960.21. Root CM, Masuyama K, Green DS, Enell LE, Nassel DR, et al. (2008) A

presynaptic gain control mechanism fine-tunes olfactory behavior. Neuron 59:

311–321.22. Stocker RF (2008) Design of the larval chemosensory system. Adv Exptl Med

Biol 628: 69–81.23. Chaudhury D, Escanilla O, Linster C (2009) Bulbar acetylcholine enhances

neural and perceptual odor discrimination. J Neurosci 29: 52–60.

Olfactory Coding in Drosophila Larvae

PLoS ONE | www.plosone.org 11 August 2011 | Volume 6 | Issue 8 | e22996